Journal of Computational Neuroscience

, Volume 43, Issue 1, pp 5–15 | Cite as

Predictive control of intersegmental tarsal movements in an insect

  • Alicia Costalago-Meruelo
  • David M. Simpson
  • Sandor M. Veres
  • Philip L. Newland


In many animals intersegmental reflexes are important for postural and movement control but are still poorly undesrtood. Mathematical methods can be used to model the responses to stimulation, and thus go beyond a simple description of responses to specific inputs. Here we analyse an intersegmental reflex of the foot (tarsus) of the locust hind leg, which raises the tarsus when the tibia is flexed and depresses it when the tibia is extended. A novel method is described to measure and quantify the intersegmental responses of the tarsus to a stimulus to the femoro-tibial chordotonal organ. An Artificial Neural Network, the Time Delay Neural Network, was applied to understand the properties and dynamics of the reflex responses. The aim of this study was twofold: first to develop an accurate method to record and analyse the movement of an appendage and second, to apply methods to model the responses using Artificial Neural Networks. The results show that Artificial Neural Networks provide accurate predictions of tarsal movement when trained with an average reflex response to Gaussian White Noise stimulation compared to linear models. Furthermore, the Artificial Neural Network model can predict the individual responses of each animal and responses to others inputs such as a sinusoid. A detailed understanding of such a reflex response could be included in the design of orthoses or functional electrical stimulation treatments to improve walking in patients with neurological disorders as well as the bio/inspired design of robots.


Reflex Artificial Neural Network Metaheuristic algorithm Evolutionary programming Particle swarm optimisation Locust Motor control 



Alicia Costalago-Meruelo was supported by an EPRSC grant (EP/G03690X/1) from The Institute of Sound and Vibration Research and the Institute for Complex Systems Simulations at the University of Southampton. The data is freely available through the Southampton University repository under. doi: 10.5258/SOTON/D0014.

Compliance with Ethical Standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Alicia Costalago-Meruelo
    • 1
    • 4
  • David M. Simpson
    • 1
  • Sandor M. Veres
    • 2
  • Philip L. Newland
    • 3
  1. 1.Faculty of Engineering and the EnvironmentUniversity of SouthamptonSouthamptonUK
  2. 2.Department of Automatic Control and Systems EngineeringUniversity of SheffieldSheffieldUK
  3. 3.Biological SciencesUniversity of SouthamptonSouthamptonUK
  4. 4.Neurologisches ForschungshausLudwig-Maximilians-UniversitätMünchenGermany

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